Eleven Multivariate Analysis Techniques:
Key Tools In Your Marketing Research Survival Kit
by
Michael RicharmeSituation 1: A harried executive walks into your office with a stack of printouts. She says, “You’re the marketing research whiz—tell me how many of this new red widget we are going to sell next year. Oh, yeah, we don’t know what price we can get for it either.” Situation 2: Another harried executive (they all seem to be that way) calls you into his office and shows you three proposed advertising campaigns for next year. He asks, “Which one should I use? They all look pretty good to me.” Situation 3: During the annual budget meeting, the sales manager wants to know why two of his main competitors are gaining share. Do they have better widgets? Do their products appeal to different types of customers? What is going on in the market? All of these situations are real, and they happen every day across corporate America. Fortunately, all of these questions are ones to which solid, quantifiable answers can be provided. An astute marketing researcher quickly develops a plan of action to address the situation. The researcher realizes that each question requires a specific type of analysis, and reaches into the analysis tool bag for. . . Over the past 20 years, the dramatic increase in desktop computing power has resulted in a corresponding increase in the availability of computation intensive statistical software. Programs like SAS and SPSS, once restricted to mainframe utilization, are now readily available in Windows-based, menu-driven packages. The marketing research analyst now has access to a much broader array of sophisticated techniques with which to explore the data. The challenge becomes knowing which technique to select, and clearly understanding their strengths and weaknesses. As my father once said to me, “If you only have a hammer, then every problem starts to look like a nail.” Overview

The purpose of this white paper is to provide an executive understanding of 11 multivariate analysis techniques, resulting in an understanding of the appropriate uses for each of the techniques. This is not a discussion of the underlying statistics of each technique; it is a field guide to understanding the types of research questions that can be formulated and the capabilities and limitations of each technique in answering those questions. In order to understand multivariate analysis, it is important to understand some of the terminology. A variate is a weighted combination of variables. The purpose of the analysis is to find the best combination of weights. Nonmetric data refers to data that are either qualitative or categorical in nature. Metric data refers to data that are quantitative, and interval or ratio in nature. Initial Step—Data Quality

Before launching into an analysis technique, it is important to have a clear understanding of the form and quality of the data. The form of the data refers to whether the data are nonmetric or metric. The quality of the data refers to how normally distributed the data are. The first few techniques discussed are sensitive to the linearity, normality, and equal variance assumptions of the data. Examinations of distribution, skewness, and kurtosis are helpful in examining distribution. Also, it is important to understand the magnitude of missing values in observations and to determine whether to ignore them or impute values to the missing observations. Another data quality measure is outliers, and it is important to determine whether the outliers should be removed. If they are kept, they may cause a distortion to the data; if they are eliminated, they may help with the assumptions of normality. The key is to attempt to understand what the outliers represent. Multiple Regression Analysis

Multiple regression is the most commonly utilized multivariate technique. It examines the relationship between a single metric dependent variable and two or more metric independent variables. The...

YOU MAY ALSO FIND THESE DOCUMENTS HELPFUL

...great effort to the whole test, I still get the further study of each test, which comes out intelligence and coordination will not apply any differences either, and the importance for the rest tests arrange according by the contribution degree is dynamometer, form relations, perseveration and dotting.
Question Summary:
Twenty engineer apprentices and twenty pilots were given six tests (Travers 1939). The variables were
Y1=intelligence y4=dotting
y2=form relations y5=sensory motor coordination
y3=dynamometer y6= perseveration
The data are recorded in the table below.
Data analysis:
Since these two dataset were independent and following normal distribution, but the variances are unknown.
For this kind of case, we see 6 variables are measured on each sampling unit in two samples. First thing came into my mind was to conduct a hypothesis test for the difference between two sample means. And we wish to test:
H0: µ1=µ2 vs. H1: µ1≠µ2 .The mean vectors and the pooled covariance matrices of the two samples are
Y1BAR
124.5
38.1
76.2
192.75
53.65
250.3
Y2BAR
129.3
31.7
87.4
236.6
44.25
280.2
Spooled
536.03158 45.021053 14.568421 51.786842 -8.684211 261.04737
45.021053 59.947368 6.4473684 -10.05 -49.70526 31.594737
14.568421 6.4473684 116 3.0315789 -3.963158 -57.96842
51.786842...

...Chapter 1
Multivariateanalysis refers to all statistical techniques that simultaneously analyze multiple measurements on individuals or objects under investigation.
Factor analysis identifies the structure underlying a set of variables
Discriminant analysis differentiates among groups based on a set of variables.
All the variables must be random and interrelated in such ways that their different effects cannot meaningfully be interpreted separately.
Nonmetric measurement scales
Nominal scales: only to identify the object.
Ordinal scales: only to indicate the order of the values.
Metric measurement scales
Interval scales: highest level of measurement precision together with ratio scales. Only differences is that interval uses an arbitrary zero point and ratio scales include an absolute zero point. Therefore it is not possible to say that any value on an interval scale is a multiple of some other point on the scale.
Ratio scales: see above.
Measurement error: is the degree to which the observed values are not representative of the “true” values. All variables used must be assumed to have some degree of measurement error.
Validity is the degree to which a measure accurately represents what it is supposed to.
Reliability if the degree to which the observed variable measures the “true” value and is “error free” Thus it is the opposite of measurement error.
In order to reduce...

...(211)---STATISTICAL TECHNIQUES FOR RISK ANALYSIS
Statistical Techniques for Risk Analysis
Statistical techniques are analytical tools for handling risky investments. These techniques, drawing from the fields of mathematics, logic, economics and psychology, enable the decision-maker to make decisions under risk or uncertainty.
The concept of probability is fundamental to the use of the riskanalysistechniques. Hoe is probability defined? How are probabilities estimated? How are they used in the risk analysistechniques? How do statistical techniques help in resolving the complex problem of analyzing risk in capital budgeting? We attempt to answer these questions in our posts.
Probability defined
The most crucial information for the capital budgeting decision is a forecast of future cash flows. A typical forecast is single figure for a period. This referred to as “best estimate” or “most likely” forecast. But the questions are: To what extent can one rely this single figure? How is this figure arrived at? Does it reflect risk? In fact, the decision analysis is limited in two ways by this single figure forecast. Firstly, we do not know the changes of this figure actually occurring, i.e. the uncertainty surrounding this figure. In other words, we do not know the range of the forecast and the...

...﻿“Eleven” Analysis
Poet Maya Angelo aptly stated, “I am convinced that most people do not grow up... We carry accumulation of years in our bodies, and on our faces, but generally our real selves, the children inside, are innocent and shy as magnolias.” Similarly, Sandra Cisneros’s “Eleven” illuminates the enigmatic journey of growing up through the sagacious eyes of an eleven year old child. As the speaker of this work asserts, the aging process does not eradicate a person’s previous self. Instead, it accumulates layers of one’s former years and creates a realistic portrait of one’s complete existence. Cisneros’s work illustrates mankind’s maddening, internal struggle as it ages in this manner. When life demands maturity, one inadvertently becomes the sobbing three year old, the introverted adolescent, or the awkward teen of one’s past. The speaker of this literary work, Rachel, embodies this frustrating process of growing up. Undoubtedly, Cisneros employs similes, repetition, and imagery as well as symbols and diction to characterize Rachel as she matures.
The similes, repetition, and imagery utilized throughout “Eleven” vividly portray the speaker. For example, Cisneros illuminates Rachel’s development with the following comparisons: “Growing old is kind of like an onion or like the rings inside a tree trunk or like my little wooden dolls that fit one inside the other.” This illustrates...

...SEVEN-ELEVEN JAPAN CO. CASE ANALYSIS
What is the future outlook for Seven Eleven Stores in USA?
Seven-Eleven is part of an international chain of convenient stores. 7-Eleven, primarily operating as a franchise, is the world's largest operator, franchisor and licensor of convenience stores, with more than 46,000 outlets.
The Seven-Eleven business model consists of five key elements:
* A differentiated merchandising strategy;
* Utilization of 7-Eleven’s retail information system & Managed distribution;
* Providing a convenient shopping environment; and
* A unique franchise model.
Let us have a brief look over 7-Eleven stores in US and Japan:
Seven-Eleven Japan:
* High density market presence with 50-60 stores supported by distribution centre.
* Limited geographical presence
* Emphasized regional merchandizing
* Processed and fast foods contributed to most of its sales
* Products like food and beverages, magazines and consumer items such as soaps and detergents
* Services offered like payment of electricity bills, telephone, gas bills, meal delivery services, 7 dream e-commerce, electronic money offering and many
* Advanced information technology helped store to analyse store data every day morning and helps in having valuable shelf life
* Information system installed in every outlet and linked to HQ, Suppliers...

...PEST ANAYLSIS 7 ELEVEN
1.1 CompanyBackground(7-Eleven)
7-Eleven, founded in 1927 in Dallas, Texas, is the world's largest operator and licenser of convenience stores with more than 21,000 units worldwide and nation's largest independent gasoline retailers. The name 7-Eleven was originated in 1946 when the stores were open from 7am to 11pm. Today, offering customers 24-hour convenience, seven days a week is the cornerstone of 7-Eleven's business.
1.2 Customer-Orientated Factors
7-Eleven focused on meeting the needs of convenience-oriented customers by providing a broad selection of fresh, high-quality products and services at everyday fair prices, speedy transactions and a clean, safe and friendly shopping environment. Each store's selection up to 2,500 different products and services is tailored to meet the needs local customers.
7-Eleven is expanding its food offerings to bring consumers a proprietary line of daily-prepared and daily delivered item and baked goods. However, its also offers consumer a number of convenience services designed to meet the unique needs of individual neighbourhoods including fax machines and automatic teller machine.
1.3 Major Players and Competitors
The major players of retailing industry include Coles , Franklins and 7-Eleven. Obviously, Coles and Franklins are the major competitors of 7-Eleven. Coles is a...